The Spam Mail Classification project is a web-based application that uses machine learning to classify emails as spam or ham. It features a Flask backend, a frontend created with HTML, CSS, and JavaScript, and a MySQL database for storing user data and email classifications.
- Email Classification: Categorizes incoming emails as spam or ham.
- User Registration and Login: Secure account creation and authentication.
- Real-Time Email Classification: Classifies emails in real time.
- User Dashboard: Users can view their email history and classifications.
- Machine Learning Model: Employs a trained model to classify emails.
- Customization: Users can configure spam filter settings.
- Flask (Python Web Framework): For the backend server.
- HTML, CSS, and JavaScript (Frontend): For the user interface.
- MySQL (Database): For storing user data and email classifications.
- Machine Learning Libraries (e.g., Scikit-Learn): Used to build and deploy the email classification model.
To use the Spam Mail Classification app, follow these steps:
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Clone this Repository: Get the project source code by cloning this repository to your local machine.
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Set Up the Flask Backend and MySQL Database:
- Refer to the documentation or instructions provided in the code for setting up the Flask backend and MySQL database.
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Install Required Python Packages:
- You'll need to install a few Python packages using pip. Open your terminal and run:
pip install Flask pip install nltk pip install mysql-connector-python
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Create a MySQL Database and Table:
- Set up the MySQL database and table by running the following SQL commands in your MySQL server:
CREATE DATABASE smc;
USE smc;
CREATE TABLE users ( id INT AUTO_INCREMENT PRIMARY KEY, full_name VARCHAR(255) NOT NULL, username VARCHAR(255) UNIQUE NOT NULL, email VARCHAR(255) UNIQUE NOT NULL, phone VARCHAR(15) NOT NULL, password VARCHAR(255) NOT NULL );
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Run the Flask App:
- Start the Flask app by running the following command in your terminal:
python app.py
- Goto browser to open this website in Localhost:
http://127.0.0.1:5000/
- Niladri Chatterjee
- Niladri Chatterjee - If others have contributed to this project, consider adding their names here.
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